On Monday, Google announced an effort called PAIR (People + AI Research)
that’s focused on studying and improving the ways that people, from researchers to consumers, interact with artificial intelligence. Whether or not PAIR
itself the answer to what ails AI, the project hits upon some major issues that need to be solved before any of the pie-in-the-sky predictions about AI can come true. These range from interpreting the results of deep learning models (which could be critical for applications in regulated industries) to optimizing the consumer user experience (which, obviously, will affect the growth of consumer AI products).
You can get some more details about the project in the Google links above, but also via posts on CNBC and WIRED:
However, I think one of the most insightful pieces I’ve read on this general topic comes from the Google Design blog, which on Sunday published a really good piece on human-centered machine learning systems
. It’s presented as a 7-point list, the first of which is so obvious we shouldn’t even need to talk about it anymore (but, of course, we do): Don’t expect Machine learning to figure out what problems to solve.
Basically, the advice is that anyone serious about building a good product should organically come across a real problem that needs solving, rather than started at AI
as the solution and then finding a problem (real or not) that AI could technically solve.
The rest of the post offers good advice on everything from figuring out whether AI is actually necessary to solve the problem (those Gmail popups reminding you to include an attachment actually use heuristics) to figuring how users interact with and expect to interact with your product. Finding the right balance on handling false negatives and false positive is also important, and varies by use case and user.
It’s really easy to think about how this type of advice applies to consumer AI products, but it’s equally as applicable to enterprise AI. For every application where automated pattern recognition on huge datasets is the answer, there’s another application where a human actually must interact with the software. And you don’t have to venture too deep into the history of IT to find instances where good-enough technologies with great UX won out over best-in-class technologies that missed the boat on UX.
Experts lately (and rightly) have been focusing a lot of energy on highlighting the importance of finding the right data on which to train AI applications, but UX should not be far behind on the list of things to think about. Today’s newsletter, for example, is full of all sorts of great ideas about applying AI everywhere from agriculture to home automation, and full of research projects in fields ranging from health care to robotics where it’s really easy to think about how it might make its way into the commercial sector. However, especially today, the path between great ideas (even technically possible ideas) and successful products almost certainly goes through UX.
On a related note: RIP Jawbone and, soon enough it seems, the idea of a mainstream fitness tracker market.